Are actions simulated or operating live on real Ledgers?

If you inject your core Project Access Token with full production permissions and leave the `env` string off `sandbox`, yes! It executes heavy Core Banking GraphQL mutations that immediately provision legally compliant EU ledgers behind the scenes.

Can the AI Agent manage physical or virtual credit cards?

Yes. Via `swan_add_virtual_card` and `swan_cancel_card` the LLM has complete programmatic control over debit and spend cards mapped to underlying accounts.

Does the system support sandbox incoming money simulation?

Yes! We expose the `swan_simulate_incoming_transfer` specifically so you can test your programmatic workflows in SandBox by pushing fake money into target ledgers.

Swan MCP Server for CrewAI 9 tools — connect in under 2 minutes

Name: Swan
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

Connect your CrewAI agents to Swan through Vinkius, pass the Edge URL in the `mcps` parameter and every Swan tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Swan Specialist",
    goal="Help users interact with Swan effectively",
    backstory=(
        "You are an expert at leveraging Swan tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Swan "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Swan MCP Server

The Swan MCP Server embeds a complete European Banking-as-a-Service architecture into Vinkius LLMs.

When paired with CrewAI, Swan becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Swan tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Automated Root Provisioning — Instantly spin up local branch operations allocating FRA or ESP IBAN formats through swan_create_account.
Card Administration — Ask the agent to generate custom virtual Mastercards assigned exclusively to distinct contractors utilizing swan_add_virtual_card.
Direct SEPA Execution — Move exact funds safely parsing external creditor data natively through swan_create_sepa_transfer directly across European networks.

The Swan MCP Server exposes 9 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Swan to CrewAI via MCP

Follow these steps to integrate the Swan MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 9 tools from Swan

Why Use CrewAI with the Swan MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Swan through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Swan + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Swan MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Swan for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Swan, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Swan tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Swan against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Swan MCP Tools for CrewAI (9)

These 9 tools become available when you connect Swan to CrewAI via MCP:

swan_add_virtual_card

Provisions a robust Mastercard Virtual Debit

swan_cancel_card

Permanently cancel a specific corporate card

swan_create_account

Requires an existing AccountHolderId. Dynamically provision a European Account under your ledger

swan_create_sepa_transfer

Initiate a standard European SEPA Credit Transfer

swan_get_accounts

List all operational Swan Bank Accounts/IBANs

swan_get_project_info

Fetch overarching details about your connected Swan Project Node

swan_get_transactions

Retrieve the ledger history for a specific Account

swan_list_cards

List all physical and virtual cards

swan_simulate_incoming_transfer

Sandbox Only - Inject fake money

Example Prompts for Swan in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Swan immediately.

"Retrieve my core project identifier and map the legal entity ID."

"Launch a brand new sub-account in France. Bind it to the root entity targeting EUR processing."

"Sweep the ledger of Account X123 and list the latest 5 transactions."

Troubleshooting Swan MCP Server with CrewAI

Common issues when connecting Swan to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Swan + CrewAI FAQ

Common questions about integrating Swan MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.